In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating "good or better", etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates. The results in this paper provide a solution to the problem of understanding the QoE distribution in a system, in cases where the necessary data is not directly available in the form of models going beyond the MOS, or where the full details of subjective experiments are not available.

中文翻译：

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从 QoS 分布到 QoE 分布：系统视角

在 QoE 管理的上下文中，网络和服务提供商通常依赖于将系统 QoS 条件（例如，系统响应时间、数据包丢失等）映射到估计的最终用户 QoE 值的模型。可以假设系统中可观察到的 QoS 条件遵循一定的分布，这意味着不同的最终用户将体验不同的条件。另一方面，根据主观用户研究的结果，我们知道用户多样性会导致任何给定测试条件下的用户分数分布（在这种情况下指的是感兴趣的 QoS 参数）。我们之前的研究表明，要在给定条件下正确推导出系统中的各种 QoE 指标（例如，平均意见得分 (MOS)、分位数、用户评价“好或更好”的概率等），需要考虑从用户研究中获得的评级分布，这通常是不可用的。在本文中，我们扩展了这些发现，以展示如何在给定 QoS 到 MOS 映射函数和二阶统计数据的情况下近似用户评分分布。然后可以将这样的用户评级分布与在系统中观察到的QoS分布组合以最终导出QoE分数的对应分布。我们提供了两个示例来说明此过程：1) 使用将等待时间与 QoE 相关联的 Web QoE 模型的分析结果，以及 2) 使用将数据包丢失与视频停顿模式相关的测量值的数值结果，这些测量值又映射到 QoE 估计值。本文的结果为理解系统中 QoE 分布的问题提供了解决方案，